Forecasting TB Incidence in Oman Using the Multilayer Pecerptron Neural Network
Abstract
In this research work, the ANN approach was applied to analyze TB
incidence in Oman. The employed annual data covers the period 2000-2018 and the
out-of-sample period ranges over the period 2019-2023. The residuals and
forecast evaluation criteria (Error, MSE and MAE) of the applied model indicate
that the model is stable in forecasting TB incidence in Oman. The results of
the study indicate that TB incidence will remain low around 5.5 cases per 100
000 population/year over the period 2019-2023. The government is encouraged to
continue on this commendable path by strengthening TB/HIV collaboration.
Country : Zimbabwe
1 Dr. Smartson. P. NYONI2 Thabani NYONI
ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
Department of Economics, University of Zimbabwe, Harare, Zimbabwe
Nyoni, S. P., & Nyoni, T.
(2019a). Forecasting TB notifications at Zengeza clinic, Zimbabwe. Online at
https://mpra.ub.uni-muenchen.de/97331/ MPRA Paper No. 97331, posted 02 Dec 2019
10:13 UTC
Nyoni, S. P., & Nyoni, T.
(2019b). Forecasting TB notifications at Silobela District Hospital, Zimbabwe.
IJARIIE 5(6): 2395-4396.
Tola Assefa., Kirubel Minsamo
Minshore., Yohanes Angele., & Abraham Nigussie Mekuria (2019).Tuberculosis
treatment outcomes and associated factors among TB patients attending Public
hospitals in Harar town Eastern Ethiopia-A five-year Retrospective study, TB
Research and treatment Centre, Addis Ababa.
